{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from ms_pred.common.plot_utils import *\n",
    "\n",
    "set_style()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mnt/c/Users/runzh/OneDrive/Documents/2023/ms-pred\n"
     ]
    }
   ],
   "source": [
    "%cd ~/ms-pred"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Fig 3 Kinetic plots on carbendazim"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "kinetics_df = pd.read_csv('data/exp_specs/pesticide/peak_area_table_919_sample_info.csv')\n",
    "\n",
    "days = kinetics_df['Time (days)'].unique()\n",
    "days.sort()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "t0_df = kinetics_df[kinetics_df['Time (days)'] == 0]\n",
    "t0_mean, t0_std = np.mean(t0_df['Normalized Peak Area']), np.std(t0_df['Normalized Peak Area'])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "light_data = [[t0_mean], [t0_std]]\n",
    "dark_data = [[t0_mean], [t0_std]]\n",
    "for day in days[1:]:\n",
    "    tmp_df = kinetics_df[kinetics_df['Time (days)'] == day]\n",
    "    light_df = tmp_df[tmp_df['Conditions'] == 'Light']\n",
    "    light_np = light_df['Normalized Peak Area'].to_numpy()\n",
    "    if len(light_np) < 3:\n",
    "        light_np = np.concatenate((light_np, np.zeros(3 - len(light_np))))\n",
    "    light_data[0].append(np.mean(light_np))\n",
    "    light_data[1].append(np.std(light_np))\n",
    "\n",
    "    dark_df = tmp_df[tmp_df['Conditions'] == 'Dark']\n",
    "    dark_np = dark_df['Normalized Peak Area'].to_numpy()\n",
    "    dark_np = np.concatenate((dark_np, np.zeros(3 - len(dark_np))))\n",
    "    dark_data[0].append(np.mean(dark_np))\n",
    "    dark_data[1].append(np.std(dark_np))\n",
    "light_data = np.array(light_data)\n",
    "dark_data = np.array(dark_data)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 280x200 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(.7, 0.5), dpi=400)\n",
    "_, caps, _ = plt.errorbar(\n",
    "    days + 1, light_data[0] / t0_mean, yerr=light_data[1] / t0_mean,\n",
    "    fmt='-o', markersize=0.5, capsize=1.5, color='#7B94CC', linewidth=0.3\n",
    ")\n",
    "for cap in caps:\n",
    "    cap.set_markeredgewidth(0.3)\n",
    "\n",
    "# set axis ticks and labels\n",
    "ax = plt.gca()\n",
    "ax.set_xscale('log')\n",
    "ax.xaxis.set_ticks(np.array([0, 1, 3, 7, 15, 35, 70]) +1)\n",
    "ax.set_xticklabels([0, 1, 3, 7, 15, 35, 70])\n",
    "ax.xaxis.set_ticks([], minor=True)\n",
    "plt.ylabel('Area (norm.)')\n",
    "ax.yaxis.set_label_coords(-0.12, 0.5)\n",
    "plt.xlabel(\"Days\")\n",
    "ax.xaxis.set_label_coords(0.5, -0.35)\n",
    "ax.set_ylim(-0.5, 7)\n",
    "\n",
    "# remove box\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "plt.savefig(\"results/figs_iceberg/919_light_kinetics.pdf\", bbox_inches=\"tight\", transparent=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 280x200 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(.7, 0.5), dpi=400)\n",
    "_, caps, _ = plt.errorbar(\n",
    "    days + 1, dark_data[0] / t0_mean, yerr=dark_data[1] / t0_mean,\n",
    "    fmt='-o', markersize=0.5, capsize=1.5, color='#7B94CC', linewidth=0.3\n",
    ")\n",
    "\n",
    "for cap in caps:\n",
    "    cap.set_markeredgewidth(0.3)\n",
    "\n",
    "# set axis ticks and labels\n",
    "ax = plt.gca()\n",
    "ax.set_xscale('log')\n",
    "ax.xaxis.set_ticks(np.array([0, 1, 3, 7, 15, 35, 70]) +1)\n",
    "ax.set_xticklabels([0, 1, 3, 7, 15, 35, 70])\n",
    "ax.xaxis.set_ticks([], minor=True)\n",
    "plt.ylabel('Area (norm.)')\n",
    "ax.yaxis.set_label_coords(-0.12, 0.5)\n",
    "plt.xlabel(\"Days\")\n",
    "ax.xaxis.set_label_coords(0.5, -0.35)\n",
    "ax.set_ylim(-0.5, 11)\n",
    "\n",
    "# remove box\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "plt.savefig(\"results/figs_iceberg/919_dark_kinetics.pdf\", bbox_inches=\"tight\", transparent=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "ms-main",
   "language": "python",
   "display_name": "ms-main"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
